{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'MultilayerPerceptronClassifier'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-786bd7f0a128>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_validation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_validation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneural_network\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMultilayerPerceptronClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'MultilayerPerceptronClassifier'"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.neural_network import MultilayerPerceptronClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "digits = load_digits()\n",
    "X_dev, X_eval, y_dev, y_eval = train_test_split(digits.data, digits.target, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "def make_scaling_lr(**params):\n",
    "    return Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', LogisticRegression(**params))          \n",
    "    ])\n",
    "\n",
    "\n",
    "def make_scaling_mlp(**params):\n",
    "    return Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', MultilayerPerceptronClassifier(**params))          \n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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vrq6KtbTe0nlr86MWizOlWkRR67FjYnemlvaBMfcq7dWrvN9ubW0Vaw8PD83H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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114a141d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_images(images, n_rows=10, n_cols=10, ordering=None):\n",
    "    if ordering is None:\n",
    "        indices = np.arange(len(images))\n",
    "    else:\n",
    "        indices = ordering.argsort()[::-1]\n",
    "    plt.figure(figsize=(1.2 * n_cols, 1.2 * n_rows))\n",
    "    plt.gray()\n",
    "    for i in range(n_rows):\n",
    "        for j in range(n_cols):\n",
    "            idx = i * n_cols + j\n",
    "            if idx < len(images):\n",
    "                idx = indices[idx]\n",
    "                plt.subplot(n_rows, n_cols, i * n_cols + j + 1)\n",
    "                plt.imshow(images[idx].reshape(8, 8), interpolation='nearest')\n",
    "                plt.axis('off')\n",
    "                if ordering is not None:\n",
    "                    plt.title(\"{:.2f}\".format(ordering[idx]))\n",
    "                \n",
    "plot_images(X_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96388888888888891"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = make_scaling_lr(C=0.1).fit(X_dev, y_dev)\n",
    "lr.score(X_eval, y_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97222222222222221"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn = make_scaling_mlp(n_hidden=100, alpha=1).fit(X_dev, y_dev)\n",
    "nn.score(X_eval, y_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.97773664343786293, 0.0035130399247694333)\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(nn, X_dev, y_dev, cv=5, n_jobs=-1)\n",
    "print(np.mean(scores), np.std(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = nn.named_steps['classifier']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the shuffle filters of the intput to hidden layer, ordered by the weight attributed by the output layer connection. Here we order the filters by their connection with the output unit for the 0 digit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EwGKxIDQ0FA8++KDpasJ6OGI9HBlVj/DwcGzevBmvvfYaAPN++sR6ODLy9QL8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\nHn/X14szeXl5OH78eLHHKUlG1qNXr15YvHgxbDY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AMDwsagEAAADA8LCoBQAA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ZHiuBKpAYmD39QCAAAAgOFhUQsAAAAAhodFLQAAAAAYHha1AAAAAGB4WNQCAAAAgOFhUQsAAAAA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\ns2hbTdYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1161b0290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images(net.coef_hidden_.T, ordering=net.coef_output_[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def shuffle_columns(X, copy=True, seed=None):\n",
    "    rng = np.random.RandomState(seed)\n",
    "    if copy:\n",
    "        X = X.copy()\n",
    "    for i in range(X.shape[1]):\n",
    "        rng.shuffle(X[:, i])\n",
    "    return X\n",
    "\n",
    "\n",
    "def make_membership_pbm(X, seed=None):\n",
    "    rng = np.random.RandomState(seed)\n",
    "    n_samples, n_features = X.shape\n",
    "    shuffle_idx = rng.permutation(2 * n_samples)\n",
    "    \n",
    "    X = np.vstack([X, shuffle_columns(X, seed=seed)])\n",
    "    y = np.concatenate([np.ones(n_samples), -np.ones(n_samples)])\n",
    "    return X[shuffle_idx], y[shuffle_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WAIDpGWoBAJieoRYAgOkZagEAmJ6hFgCA6RlqAQCYnqEWAIDpGWoBAJieoRYAgOndWrqB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\nADA9Qy0AANMz1AIAML3/C46IuZrG1EN/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11193a4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images(shuffle_columns(X_dev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2874, 64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_mem_dev, y_mem_dev = make_membership_pbm(X_dev)\n",
    "\n",
    "X_mem_dev.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.40814603847901837, 0.016766430451962418)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_val_score(make_scaling_lr(C=1), X_mem_dev, y_mem_dev, cv=5)\n",
    "scores.mean(), scores.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.97773428268444174, 0.0057505213697114314)\n",
      "CPU times: user 29.3 ms, sys: 35.6 ms, total: 64.9 ms\n",
      "Wall time: 10.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nn_1 = make_scaling_mlp(n_hidden=100, alpha=1)\n",
    "scores = cross_val_score(nn_1, X_mem_dev, y_mem_dev, cv=5, n_jobs=-1)\n",
    "print(np.mean(scores), np.std(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.72 s, sys: 3.78 s, total: 12.5 s\n",
      "Wall time: 4.47 s\n"
     ]
    }
   ],
   "source": [
    "%time _ = nn_1.fit(X_mem_dev, y_mem_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.utils.extmath import safe_sparse_dot\n",
    "\n",
    "def hidden_activate(nn, X):\n",
    "    a = safe_sparse_dot(X, nn.coef_hidden_) + nn.intercept_hidden_\n",
    "    return nn.activation_func(a)\n",
    "\n",
    "net_1 = nn_1.named_steps['classifier']\n",
    "X_dev_1 = hidden_activate(net_1, X_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VoEGD1MyZM9XQoUOtPcViZc3j4+zZs6py5crq2LFj1ppesWM98rJWPcyNYxRF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114823110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images(net_1.coef_hidden_.T, ordering=net_1.coef_output_[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_mem_dev_2, y_mem_dev_2 = make_membership_pbm(X_dev_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.99478200272685968, 0.002458645027670302)\n",
      "CPU times: user 16 ms, sys: 84 ms, total: 100 ms\n",
      "Wall time: 7.39 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nn_2 = make_scaling_mlp(n_hidden=100, alpha=1)\n",
    "scores = cross_val_score(nn_2, X_mem_dev_2, y_mem_dev_2, cv=5, n_jobs=-1)\n",
    "print(np.mean(scores), np.std(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10.7 s, sys: 80 ms, total: 10.8 s\n",
      "Wall time: 7.82 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "net_2 = nn_2.fit(X_mem_dev_2, y_mem_dev_2).named_steps['classifier']\n",
    "X_dev_2 = hidden_activate(net_2, X_dev_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the benefit of adding the new features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95823412698412702"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1),\n",
    "                X_dev, y_dev,\n",
    "                cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95825106465350385"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1),\n",
    "                np.hstack([X_dev, X_dev_1]), y_dev,\n",
    "                cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96172570654277967"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(LogisticRegression(C=1),\n",
    "                np.hstack([X_dev, X_dev_1, X_dev_2]), y_dev,\n",
    "                cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96868708865660069"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_mlp(n_hidden=100, alpha=1.),\n",
    "                np.hstack([X_dev, X_dev_1, X_dev_2]), y_dev,\n",
    "                cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A significant fraction of the signal is preserve by the successive embeddings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95685733643050719"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), X_dev_1, y_dev, cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89840060007742939"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), X_dev_2, y_dev, cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95615805265195508"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), np.hstack([X_dev_1, X_dev_2]), y_dev, cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare with random non-linear embedding features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rnd = np.random.normal(scale=1., size=(X_dev.shape[1], 200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.utils.extmath import logistic_sigmoid\n",
    "\n",
    "X_dev_proj = logistic_sigmoid(np.dot(scale(X_dev), rnd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95476432442895853"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), np.hstack([X_dev, X_dev_proj]), y_dev, cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try to use extended features for supervised learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1437"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91999999999999993"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), X_dev[:200], y_dev[:200], cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88500000000000001"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), np.hstack([X_dev, X_dev_proj])[:200], y_dev[:200], cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94000000000000006"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), np.hstack([X_dev, X_dev_1, X_dev_2])[:200], y_dev[:200], cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94999999999999996"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_mlp(alpha=1), X_dev[:200], y_dev[:200], cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96499999999999986"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_mlp(alpha=1), np.hstack([X_dev, X_dev_1, X_dev_2])[:200], y_dev[:200], cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simulating high dim problems with noisy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n_samples_dev, n_features = X_dev.shape\n",
    "n_features_highdim = 1000\n",
    "highdim_proj = np.random.normal(scale=1., size=(n_features, n_features_highdim))\n",
    "noise = np.random.normal(scale=1, size=(n_samples_dev, n_features_highdim))\n",
    "X_highdim_dev = logistic_sigmoid(np.dot(X_dev, highdim_proj)) + noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.73554732868757267"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), X_highdim_dev, y_dev, cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import RandomizedPCA\n",
    "X_highdim_pca_dev = RandomizedPCA(n_components=30).fit_transform(X_highdim_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84269986449864498"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), X_highdim_pca_dev, y_dev, cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def nn_transform(X, n_hidden=100):\n",
    "    X_mem_1, y_mem_1 = make_membership_pbm(X)\n",
    "    model_1 = make_scaling_mlp(n_hidden=n_hidden, alpha=1)\n",
    "    model_1.fit(X_mem_1, y_mem_1)\n",
    "    X_1 = hidden_activate(model_1.named_steps['classifier'], X)\n",
    "    \n",
    "    X_mem_2, y_mem_2 = make_membership_pbm(X_1)\n",
    "    model_2 = make_scaling_mlp(n_hidden=n_hidden, alpha=1)\n",
    "    model_2.fit(X_mem_2, y_mem_2)\n",
    "    X_2 = hidden_activate(model_2.named_steps['classifier'], X_1)\n",
    "    return X_1, X_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_embed_1_dev, X_embed_2_dev = nn_transform(X_highdim_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81486885404568332"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), X_embed_1_dev, y_dev, cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8176708284939993"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), np.hstack([X_embed_1_dev, X_embed_2_dev]), y_dev, cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78704752226093677"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), np.hstack([X_highdim_dev, X_embed_1_dev, X_embed_2_dev]), y_dev, cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Semi supervised predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69499999999999995"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), X_highdim_dev[:200], y_dev[:200], cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78999999999999981"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), X_highdim_pca_dev[:200], y_dev[:200], cv=5, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84499999999999997"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(make_scaling_lr(C=1), np.hstack([X_embed_1_dev, X_embed_2_dev])[:200], y_dev[:200], cv=5, n_jobs=-1).mean()"
   ]
  }
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